Association of upper airway bacterial microbiota and asthma: systematic review
Bibliographic record
Abstract
Individual studies have suggested that upper airway dysbiosis may be associated with asthma or its severity. We aimed to systematically review studies that evaluated upper airway bacterial microbiota in relation to asthma, compared to nonasthmatic controls. Searches used MEDLINE, Embase, and Web of Science Core Collection. Eligible studies included association between asthma and upper airway dysbiosis; assessment of composition and diversity of upper airway microbiota using 16S rRNA or metagenomic sequencing; upper airway samples from nose, nasopharynx, oropharynx or hypopharynx. Study quality was assessed and rated using the Newcastle-Ottawa scale. A total of 249 publications were identified; 17 in the final analysis (13 childhood asthma and 4 adult asthma). Microbiome richness was measured in 6 studies, species diversity in 12, and bacterial composition in 17. The quality of evidence was good and fair. The alpha-diversity was found to be higher in younger children with wheezing and asthma, while it was lower when asthmatic children had rhinitis or mite sensitization. In children, Proteobacteria and Firmicutes were higher in asthmatics compared to controls (7 studies), and Moraxella , Streptococcus , and Haemophilus were predominant in the bacterial community. In pooled analysis, nasal Streptococcus colonization was associated with the presence of wheezing at age 5 ( p = 0.04). In adult patients with asthma, the abundance of Proteobacteria was elevated in the upper respiratory tract (3 studies). Nasal colonization of Corynebacterium was lower in asthmatics (2 studies). This study demonstrates the potential relationships between asthma and specific bacterial colonization in the upper airway in adult and children with asthma.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".